



'''simple docstring'''



class 						lowerCamelCase_				:







						def __init__(	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      0
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      0
													SCREAMING_SNAKE_CASE       :   List[str]         =      {}







						def        __lowercase     (	self :   List[Any]					,     lowerCAmelCase__ :   List[str]					):

													"""simple docstring"""
													if vertex not in self.adjacency:
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      {}
																				self.num_vertices += 1







						def        __lowercase     (	self :   Optional[int]					,     lowerCAmelCase__ :   int					,     lowerCAmelCase__ :   Any					,     lowerCAmelCase__ :   Optional[int]					):

													"""simple docstring"""
													self.add_vertex(lowerCAmelCase__					)
													self.add_vertex(lowerCAmelCase__					)

													if head == tail:
																				return

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      weight
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      weight







						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.get_edges()
													for edge in edges:
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Dict         =      edge
																				edges.remove((tail, head, weight)					)
													for i in range(len(lowerCAmelCase__					)					):
																				SCREAMING_SNAKE_CASE       :   str         =      list(edges[i]					)

													edges.sort(key=lambda lowerCAmelCase__					: e[2]					)
													for i in range(len(lowerCAmelCase__					) - 1					):
																				if edges[i][2] >= edges[i + 1][2]:
																											SCREAMING_SNAKE_CASE       :   Tuple         =      edges[i][2] + 1
													for edge in edges:
																				SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      edge
																				SCREAMING_SNAKE_CASE       :   Dict         =      weight
																				SCREAMING_SNAKE_CASE       :   Dict         =      weight







						def __str__(	self :   List[str]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      ''''''
													for tail in self.adjacency:
																				for head in self.adjacency[tail]:
																											SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.adjacency[head][tail]
																											string += F"""{head} -> {tail} == {weight}\n"""
													return string.rstrip('''\n'''					)







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      []
													for tail in self.adjacency:
																				for head in self.adjacency[tail]:
																											output.append((tail, head, self.adjacency[head][tail])					)
													return output







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return self.adjacency.keys()







						@staticmethod
						def        __lowercase     (	lowerCAmelCase__ :   str=None					,     lowerCAmelCase__ :   int=None					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      Graph()
													if vertices is None:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      []
													if edges is None:
																				SCREAMING_SNAKE_CASE       :   Tuple         =      []
													for vertex in vertices:
																				g.add_vertex(lowerCAmelCase__					)
													for edge in edges:
																				g.add_edge(*lowerCAmelCase__					)
													return g
						class 						lowerCamelCase_				:







												def __init__(	self :   Optional[Any]					):

																			"""simple docstring"""
																			SCREAMING_SNAKE_CASE       :   Optional[int]         =      {}
																			SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {}







												def __len__(	self :   int					):

																			"""simple docstring"""
																			return len(self.parent					)







												def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   Optional[int]					):

																			"""simple docstring"""
																			if item in self.parent:
																										return self.find(lowerCAmelCase__					)

																			SCREAMING_SNAKE_CASE       :   Dict         =      item
																			SCREAMING_SNAKE_CASE       :   List[Any]         =      0
																			return item







												def        __lowercase     (	self :   Tuple					,     lowerCAmelCase__ :   List[str]					):

																			"""simple docstring"""
																			if item not in self.parent:
																										return self.make_set(lowerCAmelCase__					)
																			if item != self.parent[item]:
																										SCREAMING_SNAKE_CASE       :   Any         =      self.find(self.parent[item]					)
																			return self.parent[item]







												def        __lowercase     (	self :   Any					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Optional[int]					):

																			"""simple docstring"""
																			SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      self.find(lowerCAmelCase__					)
																			SCREAMING_SNAKE_CASE       :   Dict         =      self.find(lowerCAmelCase__					)

																			if roota == roota:
																										return roota

																			if self.rank[roota] > self.rank[roota]:
																										SCREAMING_SNAKE_CASE       :   List[str]         =      roota
																										return roota

																			if self.rank[roota] < self.rank[roota]:
																										SCREAMING_SNAKE_CASE       :   List[Any]         =      roota
																										return roota

																			if self.rank[roota] == self.rank[roota]:
																										self.rank[roota] += 1
																										SCREAMING_SNAKE_CASE       :   Any         =      roota
																										return roota
																			return None







						@staticmethod
						def        __lowercase     (	lowerCAmelCase__ :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Dict         =      graph.num_vertices

													SCREAMING_SNAKE_CASE       :   Dict         =      Graph.UnionFind()
													SCREAMING_SNAKE_CASE       :   List[Any]         =      []
													while num_components > 1:
																				SCREAMING_SNAKE_CASE       :   Optional[int]         =      {}
																				for vertex in graph.get_vertices():
																											SCREAMING_SNAKE_CASE       :   Dict         =      -1

																				SCREAMING_SNAKE_CASE       :   Tuple         =      graph.get_edges()
																				for edge in edges:
																											SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      edge
																											edges.remove((tail, head, weight)					)
																				for edge in edges:
																											SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      edge
																											SCREAMING_SNAKE_CASE       :   str         =      union_find.find(lowerCAmelCase__					)
																											SCREAMING_SNAKE_CASE       :   str         =      union_find.find(lowerCAmelCase__					)
																											if seta != seta:
																																		if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																									SCREAMING_SNAKE_CASE       :   Optional[int]         =      [head, tail, weight]

																																		if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
																																									SCREAMING_SNAKE_CASE       :   Tuple         =      [head, tail, weight]
																				for vertex in cheap_edge:
																											if cheap_edge[vertex] != -1:
																																		SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   List[Any]         =      cheap_edge[vertex]
																																		if union_find.find(lowerCAmelCase__					) != union_find.find(lowerCAmelCase__					):
																																									union_find.union(lowerCAmelCase__					,     lowerCAmelCase__					)
																																									mst_edges.append(cheap_edge[vertex]					)
																																									SCREAMING_SNAKE_CASE       :   int         =      num_components - 1
													SCREAMING_SNAKE_CASE       :   List[str]         =      Graph.build(edges=lowerCAmelCase__					)
													return mst









'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : str						  =					KandinskyInpaintPipeline
						_lowerCAmelCase  : List[Any]						  =					['prompt', 'image_embeds', 'negative_image_embeds', 'image', 'mask_image']
						_lowerCAmelCase  : int						  =					[
						    'prompt',
						    'negative_prompt',
						    'image_embeds',
						    'negative_image_embeds',
						    'image',
						    'mask_image',
						]
						_lowerCAmelCase  : List[str]						  =					[
						    'generator',
						    'height',
						    'width',
						    'latents',
						    'guidance_scale',
						    'negative_prompt',
						    'num_inference_steps',
						    'return_dict',
						    'guidance_scale',
						    'num_images_per_prompt',
						    'output_type',
						    'return_dict',
						]
						_lowerCAmelCase  : Union[str, Any]						  =					False







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													return 32







						@property
						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													return self.time_input_dim







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.time_input_dim * 4







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return 1_00







						@property
						def        __lowercase     (	self :   Tuple					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
													return tokenizer







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      MCLIPConfig(
													    numDims=self.cross_attention_dim					,     transformerDimensions=self.text_embedder_hidden_size					,     hidden_size=self.text_embedder_hidden_size					,     intermediate_size=37					,     num_attention_heads=4					,     num_hidden_layers=5					,     vocab_size=10_05					,     )

													SCREAMING_SNAKE_CASE       :   Any         =      MultilingualCLIP(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      text_encoder.eval()

													return text_encoder







						@property
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)

													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''in_channels''': 9,
													    # Out channels is double in channels because predicts mean and variance
													    '''out_channels''': 8,
													    '''addition_embed_type''': '''text_image''',
													    '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
													    '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
													    '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
													    '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
													    '''layers_per_block''': 1,
													    '''encoder_hid_dim''': self.text_embedder_hidden_size,
													    '''encoder_hid_dim_type''': '''text_image_proj''',
													    '''cross_attention_dim''': self.cross_attention_dim,
													    '''attention_head_dim''': 4,
													    '''resnet_time_scale_shift''': '''scale_shift''',
													    '''class_embed_type''': None,
													}

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      UNetaDConditionModel(**lowerCAmelCase__					)
													return model







						@property
						def        __lowercase     (	self :   Optional[Any]					):

													"""simple docstring"""
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}







						@property
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													torch.manual_seed(0					)
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      VQModel(**self.dummy_movq_kwargs					)
													return model







						def        __lowercase     (	self :   Optional[int]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      self.dummy_text_encoder
													SCREAMING_SNAKE_CASE       :   Tuple         =      self.dummy_tokenizer
													SCREAMING_SNAKE_CASE       :   int         =      self.dummy_unet
													SCREAMING_SNAKE_CASE       :   str         =      self.dummy_movq

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      DDIMScheduler(
													    num_train_timesteps=10_00					,     beta_schedule='''linear'''					,     beta_start=0.0_0085					,     beta_end=0.012					,     clip_sample=lowerCAmelCase__					,     set_alpha_to_one=lowerCAmelCase__					,     steps_offset=1					,     prediction_type='''epsilon'''					,     thresholding=lowerCAmelCase__					,     )

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      {
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''movq''': movq,
													}

													return components







						def        __lowercase     (	self :   Dict					,     lowerCAmelCase__ :   Optional[Any]					,     lowerCAmelCase__ :   List[Any]=0					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   List[Any]         =      floats_tensor((1, self.cross_attention_dim)					,     rng=random.Random(seed + 1					)					).to(lowerCAmelCase__					)
													# create init_image
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      floats_tensor((1, 3, 64, 64)					,     rng=random.Random(lowerCAmelCase__					)					).to(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      image.cpu().permute(0					,     2					,     3					,     1					)[0]
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      Image.fromarray(np.uinta(lowerCAmelCase__					)					).convert('''RGB'''					).resize((2_56, 2_56)					)
													# create mask
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((64, 64)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   str         =      0

													if str(lowerCAmelCase__					).startswith('''mps'''					):
																				SCREAMING_SNAKE_CASE       :   Tuple         =      torch.manual_seed(lowerCAmelCase__					)
													else:
																				SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      torch.Generator(device=lowerCAmelCase__					).manual_seed(lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   Tuple         =      {
													    '''prompt''': '''horse''',
													    '''image''': init_image,
													    '''mask_image''': mask,
													    '''image_embeds''': image_embeds,
													    '''negative_image_embeds''': negative_image_embeds,
													    '''generator''': generator,
													    '''height''': 64,
													    '''width''': 64,
													    '''num_inference_steps''': 2,
													    '''guidance_scale''': 4.0,
													    '''output_type''': '''np''',
													}
													return inputs







						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Any         =      '''cpu'''

													SCREAMING_SNAKE_CASE       :   Dict         =      self.get_dummy_components()

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.pipeline_class(**lowerCAmelCase__					)
													SCREAMING_SNAKE_CASE       :   int         =      pipe.to(lowerCAmelCase__					)

													pipe.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   str         =      pipe(**self.get_dummy_inputs(lowerCAmelCase__					)					)
													SCREAMING_SNAKE_CASE       :   int         =      output.images

													SCREAMING_SNAKE_CASE       :   int         =      pipe(
													    **self.get_dummy_inputs(lowerCAmelCase__					)					,     return_dict=lowerCAmelCase__					,     )[0]

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image[0, -3:, -3:, -1]
													SCREAMING_SNAKE_CASE       :   Dict         =      image_from_tuple[0, -3:, -3:, -1]

													print(F"""image.shape {image.shape}"""					)

													assert image.shape == (1, 64, 64, 3)

													SCREAMING_SNAKE_CASE       :   List[str]         =      np.array(
													    [0.832_6919, 0.7379_0467, 0.2091_8581, 0.930_9612, 0.551_1791, 0.4371_3328, 0.551_3321, 0.4992_2934, 0.5949_7786]					)

													assert (
													    np.abs(image_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice					).max() < 1e-2
													), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													super().test_inference_batch_single_identical(expected_max_diff=3e-3					)


@slow
@require_torch_gpu
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def        __lowercase     (	self :   List[str]					):

													"""simple docstring"""
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()







						def        __lowercase     (	self :   Dict					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   List[Any]         =      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''					)

													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      np.ones((7_68, 7_68)					,     dtype=np.floataa					)
													SCREAMING_SNAKE_CASE       :   List[str]         =      0

													SCREAMING_SNAKE_CASE       :   Dict         =      '''a hat'''

													SCREAMING_SNAKE_CASE       :   str         =      KandinskyPriorPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-prior'''					,     torch_dtype=torch.floataa					)
													pipe_prior.to(lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      KandinskyInpaintPipeline.from_pretrained(
													    '''kandinsky-community/kandinsky-2-1-inpaint'''					,     torch_dtype=torch.floataa					)
													SCREAMING_SNAKE_CASE       :   Dict         =      pipeline.to(lowerCAmelCase__					)
													pipeline.set_progress_bar_config(disable=lowerCAmelCase__					)

													SCREAMING_SNAKE_CASE       :   Optional[int]         =      torch.Generator(device='''cpu'''					).manual_seed(0					)
													SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   Any         =      pipe_prior(
													    lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=5					,     negative_prompt=''''''					,     ).to_tuple()

													SCREAMING_SNAKE_CASE       :   Tuple         =      pipeline(
													    lowerCAmelCase__					,     image=lowerCAmelCase__					,     mask_image=lowerCAmelCase__					,     image_embeds=lowerCAmelCase__					,     negative_image_embeds=lowerCAmelCase__					,     generator=lowerCAmelCase__					,     num_inference_steps=1_00					,     height=7_68					,     width=7_68					,     output_type='''np'''					,     )

													SCREAMING_SNAKE_CASE       :   Tuple         =      output.images[0]

													assert image.shape == (7_68, 7_68, 3)

													assert_mean_pixel_difference(lowerCAmelCase__					,     lowerCAmelCase__					)




